
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt


n =10 
p=0.05
k=np.arange(0,n+1)
binomial = stats.binom.pmf(k,n,p)

print("Question 2 : ANSWERS: \n")
print(binomial)
# Probability that none of the items are defective?
print(f"\nA: Probability that none of the items are defective : {binomial[0] } \n")

#--------------------------------------------------------------------------------------------------
# Probability that exactly one of the items is defective?
print(f"B: Probability that exactly one of the items is defective : {binomial[1] } \n")


#--------------------------------------------------------------------------------------------------
# Probability that two or fewer of the items are defective? 
"""
P("two or fewer of the items are defective" ) = P(No defective) + P(1 defective ) + P(2 defective)
                                              = binomial[0] + binomial[1] + binomial[2]
"""
probability_two_or_fewer_defective = binomial[0] + binomial[1] + binomial[2]
print(f"C: Probability that two or fewer of the items are defective?  : {probability_two_or_fewer_defective } \n")


#--------------------------------------------------------------------------------------------------
# Probability that three or more of the items are defective ?
"""
P("three or more of the items are defective" ) = P(3 defective) + ..... +P(10 defective )                                               
                                              = binomial[3] + binomial[4] + binomial[5] + binomial[6]
 + binomial[7] + binomial[8]  + binomial[9]  + binomial[10]
 
                                             OR
                                             = 1-(probability_two_or_fewer_defective)
 
 """
probability_three_or_more_defective = binomial[3] + binomial[4] + binomial[5] + binomial[6] + binomial[7] + binomial[8]  + binomial[9]  + binomial[10]
print(f"D: Probability that three or more of the items are defective ? : {probability_three_or_more_defective } \n")
print(f"\n OR \n Probability that three or more of the items are defective = 1-(probability_two_or_fewer_defective) :{1-probability_two_or_fewer_defective}")


#--------------------------------------------------------------------------------------------------

Question 2 : ANSWERS: 

[5.98736939e-01 3.15124705e-01 7.46347985e-02 1.04750594e-02
 9.64808106e-04 6.09352488e-05 2.67259863e-06 8.03789063e-08
 1.58642578e-09 1.85546875e-11 9.76562500e-14]

A: Probability that none of the items are defective : 0.5987369392383789 

B: Probability that exactly one of the items is defective : 0.31512470486230504 

C: Probability that two or fewer of the items are defective?  : 0.9884964426207035 

D: Probability that three or more of the items are defective ? : 0.011503557379296881 


 OR 
 Probability that three or more of the items are defective = 1-(probability_two_or_fewer_defective) :0.011503557379296536


rate = 3
n=np.arange(0,10)
poisson = stats.poisson.pmf(n,rate)

print("Question 3 : ANSWERS: \n")
print(poisson)

#--------------------------------------------------------------------------------------------------
# A. Probability that in a given week he will sell some cars.
"""
P(sell some cars in week) = 1-P(no cars sell in week)
                          = 1- poisson[0]
                          = 1- 0.04978707
                          = 0.9502129
"""
print(f"\n A : Probability that in a given week he will sell some cars : {1- poisson[0] } \n")

#--------------------------------------------------------------------------------------------------
# B. Probability that in a given week he will sell 2 or more but less than 5 cars.
"""
P(sell 2 or more but less than 5 cars) = P(sell 2 cars) + P(sell 3 cars) + P(sell 4 cars)
                          = poisson[2] + poisson[3] + poisson[4]
                          = 0.22404181 + 0.22404181 + 0.16803136
                          = 0.61611498
"""
print(f"\n B : Probability that in a given week he will sell 2 or more but less than 5 cars. : {poisson[2] + poisson[3] + poisson[4] } \n")

#--------------------------------------------------------------------------------------------------
# C. Plot the poisson distribution function for cumulative probability of cars sold per-week vs number of cars sold per-week.

print(f"\n C : Plot the poisson distribution function for cumulative probability of cars sold per-week vs number of cars sold per-week.  \n")
plt.plot(n,poisson,'o-')
plt.title('Poisson: $\lambda$ = %i ' % rate)
plt.xlabel('Number of cars sold per-week')
plt.ylabel('Probability of cars sold per-week')
plt.show()
#--------------------------------------------------------------------------------------------------

Question 3 : ANSWERS: 

[0.04978707 0.14936121 0.22404181 0.22404181 0.16803136 0.10081881
 0.05040941 0.02160403 0.00810151 0.0027005 ]

 A : Probability that in a given week he will sell some cars : 0.950212931632136 


 B : Probability that in a given week he will sell 2 or more but less than 5 cars. : 0.6161149710523164 


 C : Plot the poisson distribution function for cumulative probability of cars sold per-week vs number of cars sold per-week.


size =3 
p_correct=0.868
orders=np.arange(0,size+1)
bin_order = stats.binom.pmf(orders,size,p_correct)


print("Question 4 : ANSWERS: \n")
print(bin_order)

#--------------------------------------------------------------------------------------------------

# A. What is the probability that all three orders will be recognised correctly?
print(f"\nA: Probability that all three orders will be recognised correctly : {bin_order[3] } \n")

#--------------------------------------------------------------------------------------------------

# B. What is the probability that none of the three orders will be recognised correctly?
print(f"\nB: Probability that none of the three orders will be recognised correctly : {bin_order[0] } \n")

#--------------------------------------------------------------------------------------------------
# C. What is the probability that at least two of the three orders will be recognised correctly?
print(f"\nC: Probability that at least two of the three orders will be recognised correctly : {bin_order[2] + bin_order[3]} \n")

Question 4 : ANSWERS: 

[0.00229997 0.0453721  0.2983559  0.65397203]

A: Probability that all three orders will be recognised correctly : 0.653972032 


B: Probability that none of the three orders will be recognised correctly : 0.002299968 


C: Probability that at least two of the three orders will be recognised correctly : 0.9523279359999999


normal_n = 300
mean = 60
std = 12 

print("Question 5 : ANSWERS: \n")


#--------------------------------------------------------------------------------------------------

# A. What is the percentage of students who score more than 80.

z_more_than_80= (80 - 60)/12 
percentage_of_students_score_more_than_80 = stats.norm.cdf(z_more_than_80)

print("A: percentage of students who score more than 80 by z score calculation = ")
print((1- percentage_of_students_score_more_than_80)*100 )

print(f"\n percentage of students who score more than 80 : {(1 - stats.norm.cdf(80,loc=60,scale=12)) *100 } % \n")

#--------------------------------------------------------------------------------------------------

# B. What is the percentage of students who score less than 50.

z_less_than_50= (50 - 60)/12 
percentage_of_students_less_than_50= stats.norm.cdf(z_less_than_50)

print("B: percentage of students who score less than 50 = ")
print((percentage_of_students_less_than_50)*100 )

print(f"\n percentage of students who score less than 50 : {(stats.norm.cdf(50,loc=60,scale=12)) *100 } % \n")

#--------------------------------------------------------------------------------------------------
# C. What should be the distinction mark if the highest 10% of students are to be awarded distinction?

import scipy.stats as st
z_90_percent = st.norm.ppf(.90)

x= z_90_percent * std + mean

print(f"C: Distinction mark if the highest 10% of students are to be awarded distinction is : {x}")

print("\n\nVerification : ")
print(f"percentage at value > x : {1- stats.norm.cdf(x,loc=60,scale=12)}")

Question 5 : ANSWERS: 

A: percentage of students who score more than 80 by z score calculation = 
4.77903522728147

 percentage of students who score more than 80 : 4.77903522728147 % 

B: percentage of students who score less than 50 = 
20.232838096364308

 percentage of students who score less than 50 : 20.232838096364308 % 

C: Distinction mark if the highest 10% of students are to be awarded distinction is : 75.3786187865352


Verification : 
percentage at value > x : 0.10000000000000009


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True) # adds a nice background to the graphs
%matplotlib inline


Data  = pd.read_csv('DS - Part2 - Basketball.csv') # Import the dataset named 'Admission_predict.csv'
Data.head()  # view the first 5 rows of the data


Data.shape # see the shape of the data

(61, 13)


Data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 13 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   Team                 61 non-null     object
 1   Tournament           61 non-null     int64 
 2   Score                61 non-null     object
 3   PlayedGames          61 non-null     object
 4   WonGames             61 non-null     object
 5   DrawnGames           61 non-null     object
 6   LostGames            61 non-null     object
 7   BasketScored         61 non-null     object
 8   BasketGiven          61 non-null     object
 9   TournamentChampion   61 non-null     object
 10  Runner-up            61 non-null     object
 11  TeamLaunch           61 non-null     object
 12  HighestPositionHeld  61 non-null     int64 
dtypes: int64(2), object(11)
memory usage: 6.3+ KB


Data.isnull().sum().sum()

0


dupes = Data.duplicated()
sum(dupes)

0


Data.isnull().values.any()   # Any of the values in the dataframe is a missing value

False


Data.tail()


Data.TournamentChampion.value_counts()

-     52
1      3
2      1
25     1
33     1
8      1
10     1
6      1
Name: TournamentChampion, dtype: int64


Data['Runner-up'].value_counts()

-     48
1      5
23     1
6      1
25     1
3      1
5      1
8      1
7      1
4      1
Name: Runner-up, dtype: int64


Data.Score.replace('-',0,inplace= True)
Data.PlayedGames.replace('-',0,inplace= True)
Data.WonGames.replace('-',0,inplace= True)
Data.DrawnGames.replace('-',0,inplace= True)
Data.LostGames.replace('-',0,inplace= True)
Data.BasketScored.replace('-',0,inplace= True)
Data.BasketGiven.replace('-',0,inplace= True)
Data.TournamentChampion.replace('-',0,inplace= True)
Data['Runner-up'].replace('-',0,inplace= True)


Data.tail()


Data["TeamLaunchStartYear"] = Data["TeamLaunch"].apply(lambda x : x.split("-")[0])
Data["TeamLaunchStartYear"] = Data["TeamLaunchStartYear"].apply(lambda x : x.split("~")[0])
Data["TeamLaunchStartYear"] = Data["TeamLaunchStartYear"].apply(lambda x : x.split("to")[0])
Data["TeamLaunchStartYear"] = Data["TeamLaunchStartYear"].apply(lambda x : x.split("_")[0])

Data


Data.columns
Data[['Tournament', 'Score', 'PlayedGames', 'WonGames', 'DrawnGames', 'LostGames', 'BasketScored', 'BasketGiven', 'TournamentChampion',
       'Runner-up', 'HighestPositionHeld','TeamLaunchStartYear']] = Data[['Tournament', 'Score', 'PlayedGames', 'WonGames', 'DrawnGames',
       'LostGames', 'BasketScored', 'BasketGiven', 'TournamentChampion','Runner-up', 'HighestPositionHeld',
       'TeamLaunchStartYear']].apply(pd.to_numeric)

Data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 14 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   Team                 61 non-null     object
 1   Tournament           61 non-null     int64 
 2   Score                61 non-null     int64 
 3   PlayedGames          61 non-null     int64 
 4   WonGames             61 non-null     int64 
 5   DrawnGames           61 non-null     int64 
 6   LostGames            61 non-null     int64 
 7   BasketScored         61 non-null     int64 
 8   BasketGiven          61 non-null     int64 
 9   TournamentChampion   61 non-null     int64 
 10  Runner-up            61 non-null     int64 
 11  TeamLaunch           61 non-null     object
 12  HighestPositionHeld  61 non-null     int64 
 13  TeamLaunchStartYear  61 non-null     int64 
dtypes: int64(12), object(2)
memory usage: 6.8+ KB


Data["Percentage_won"] = (Data["WonGames"]/Data["PlayedGames"])* 100
Data["Percentage_BasketScored"] = (Data["BasketScored"]/(Data["BasketScored"] + Data["BasketGiven"]))* 100
Data["Finalist"] = (Data["TournamentChampion"]+Data["Runner-up"])
Data["percentage_Finalist"] = ((Data["TournamentChampion"] + Data["Runner-up"])/Data["Tournament"])* 100
Data["percentage_TournamentChampion"] = (Data["TournamentChampion"]/Data["Tournament"])* 100
Data


Data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 19 columns):
 #   Column                         Non-Null Count  Dtype  
---  ------                         --------------  -----  
 0   Team                           61 non-null     object 
 1   Tournament                     61 non-null     int64  
 2   Score                          61 non-null     int64  
 3   PlayedGames                    61 non-null     int64  
 4   WonGames                       61 non-null     int64  
 5   DrawnGames                     61 non-null     int64  
 6   LostGames                      61 non-null     int64  
 7   BasketScored                   61 non-null     int64  
 8   BasketGiven                    61 non-null     int64  
 9   TournamentChampion             61 non-null     int64  
 10  Runner-up                      61 non-null     int64  
 11  TeamLaunch                     61 non-null     object 
 12  HighestPositionHeld            61 non-null     int64  
 13  TeamLaunchStartYear            61 non-null     int64  
 14  Percentage_won                 60 non-null     float64
 15  Percentage_BasketScored        60 non-null     float64
 16  Finalist                       61 non-null     int64  
 17  percentage_Finalist            61 non-null     float64
 18  percentage_TournamentChampion  61 non-null     float64
dtypes: float64(4), int64(13), object(2)
memory usage: 9.2+ KB


Data.describe()


print(f"Mean: \n{Data.mean()}")

Mean: 
Tournament                         24.000000
Score                             901.426230
PlayedGames                       796.819672
WonGames                          303.967213
DrawnGames                        188.934426
LostGames                         303.754098
BasketScored                     1140.344262
BasketGiven                      1140.229508
TournamentChampion                  1.426230
Runner-up                           1.409836
HighestPositionHeld                 7.081967
TeamLaunchStartYear              1958.918033
Percentage_won                     31.364790
Percentage_BasketScored            43.610198
Finalist                            2.836066
percentage_Finalist                 3.645135
percentage_TournamentChampion       1.720841
dtype: float64


print(f"Median:\n{Data.median()}")

Median:
Tournament                         12.000000
Score                             375.000000
PlayedGames                       423.000000
WonGames                          123.000000
DrawnGames                         95.000000
LostGames                         197.000000
BasketScored                      430.000000
BasketGiven                       632.000000
TournamentChampion                  0.000000
Runner-up                           0.000000
HighestPositionHeld                 6.000000
TeamLaunchStartYear              1951.000000
Percentage_won                     30.491722
Percentage_BasketScored            42.399042
Finalist                            0.000000
percentage_Finalist                 0.000000
percentage_TournamentChampion       0.000000
dtype: float64


print("Data_quantile(25%):\n",Data.quantile(q=0.25))

Data_quantile(25%):
 Tournament                          4.000000
Score                              96.000000
PlayedGames                       114.000000
WonGames                           34.000000
DrawnGames                         24.000000
LostGames                          62.000000
BasketScored                      153.000000
BasketGiven                       221.000000
TournamentChampion                  0.000000
Runner-up                           0.000000
HighestPositionHeld                 3.000000
TeamLaunchStartYear              1935.000000
Percentage_won                     27.607494
Percentage_BasketScored            39.742084
Finalist                            0.000000
percentage_Finalist                 0.000000
percentage_TournamentChampion       0.000000
Name: 0.25, dtype: float64


print("Data_quantile(75%):\n",Data.quantile(q=0.75))

Data_quantile(75%):
 Tournament                         38.000000
Score                            1351.000000
PlayedGames                      1318.000000
WonGames                          426.000000
DrawnGames                        330.000000
LostGames                         563.000000
BasketScored                     1642.000000
BasketGiven                      1951.000000
TournamentChampion                  0.000000
Runner-up                           0.000000
HighestPositionHeld                10.000000
TeamLaunchStartYear              1978.000000
Percentage_won                     33.540164
Percentage_BasketScored            45.493620
Finalist                            0.000000
percentage_Finalist                 0.000000
percentage_TournamentChampion       0.000000
Name: 0.75, dtype: float64


sns.boxplot(x=Data["Score"])

<AxesSubplot:xlabel='Score'>


Data.quantile(0.75) - Data.quantile(0.25)

Tournament                         34.000000
Score                            1255.000000
PlayedGames                      1204.000000
WonGames                          392.000000
DrawnGames                        306.000000
LostGames                         501.000000
BasketScored                     1489.000000
BasketGiven                      1730.000000
TournamentChampion                  0.000000
Runner-up                           0.000000
HighestPositionHeld                 7.000000
TeamLaunchStartYear                43.000000
Percentage_won                      5.932670
Percentage_BasketScored             5.751537
Finalist                            0.000000
percentage_Finalist                 0.000000
percentage_TournamentChampion       0.000000
dtype: float64


data_values= Data.drop(['Team','TeamLaunch'], 1)
print(data_values.max() - data_values.min())

Tournament                         85.000000
Score                            4385.000000
PlayedGames                      2762.000000
WonGames                         1647.000000
DrawnGames                        633.000000
LostGames                        1070.000000
BasketScored                     5947.000000
BasketGiven                      3889.000000
TournamentChampion                 33.000000
Runner-up                          25.000000
HighestPositionHeld                19.000000
TeamLaunchStartYear                88.000000
Percentage_won                     42.964036
Percentage_BasketScored            37.675961
Finalist                           56.000000
percentage_Finalist                65.116279
percentage_TournamentChampion      38.372093
dtype: float64


print(Data.var())

Tournament                       7.197000e+02
Score                            1.287996e+06
PlayedGames                      7.678715e+05
WonGames                         1.656417e+05
DrawnGames                       4.072303e+04
LostGames                        8.685316e+04
BasketScored                     2.270266e+06
BasketGiven                      1.354223e+06
TournamentChampion               2.994863e+01
Runner-up                        2.061257e+01
HighestPositionHeld              2.784317e+01
TeamLaunchStartYear              7.553765e+02
Percentage_won                   6.132769e+01
Percentage_BasketScored          4.593064e+01
Finalist                         9.883934e+01
percentage_Finalist              1.361890e+02
percentage_TournamentChampion    4.086626e+01
dtype: float64


print(Data.std())

Tournament                         26.827225
Score                            1134.899121
PlayedGames                       876.282765
WonGames                          406.991030
DrawnGames                        201.799477
LostGames                         294.708594
BasketScored                     1506.740211
BasketGiven                      1163.710766
TournamentChampion                  5.472535
Runner-up                           4.540107
HighestPositionHeld                 5.276663
TeamLaunchStartYear                27.484114
Percentage_won                      7.831199
Percentage_BasketScored             6.777215
Finalist                            9.941798
percentage_Finalist                11.670006
percentage_TournamentChampion       6.392672
dtype: float64


Data.cov()


Data.corr()


sns.pairplot(Data, kind="reg")

<seaborn.axisgrid.PairGrid at 0x7f92bf519370>


fig,ax = plt.subplots(figsize=(10, 10))   
sns.heatmap(Data.corr(), ax=ax, annot=True, linewidths=0.05, fmt= '.2f',cmap="magma") 
plt.show()


Data.skew()

Tournament                       1.217038
Score                            1.593109
PlayedGames                      1.141978
WonGames                         1.805728
DrawnGames                       1.004159
LostGames                        0.897130
BasketScored                     1.777436
BasketGiven                      0.975859
TournamentChampion               4.777021
Runner-up                        4.360643
HighestPositionHeld              0.817976
TeamLaunchStartYear              0.672956
Percentage_won                   1.440046
Percentage_BasketScored          1.189488
Finalist                         4.562439
percentage_Finalist              4.374451
percentage_TournamentChampion    4.702287
dtype: float64


plt.hist(Data['Score'], bins=50)

(array([14.,  7.,  4.,  4.,  4.,  2.,  3.,  2.,  0.,  0.,  0.,  2.,  0.,
         2.,  1.,  2.,  2.,  0.,  0.,  0.,  2.,  1.,  0.,  0.,  1.,  0.,
         0.,  0.,  0.,  1.,  0.,  1.,  1.,  0.,  0.,  0.,  0.,  0.,  2.,
         1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  1.]),
 array([   0. ,   87.7,  175.4,  263.1,  350.8,  438.5,  526.2,  613.9,
         701.6,  789.3,  877. ,  964.7, 1052.4, 1140.1, 1227.8, 1315.5,
        1403.2, 1490.9, 1578.6, 1666.3, 1754. , 1841.7, 1929.4, 2017.1,
        2104.8, 2192.5, 2280.2, 2367.9, 2455.6, 2543.3, 2631. , 2718.7,
        2806.4, 2894.1, 2981.8, 3069.5, 3157.2, 3244.9, 3332.6, 3420.3,
        3508. , 3595.7, 3683.4, 3771.1, 3858.8, 3946.5, 4034.2, 4121.9,
        4209.6, 4297.3, 4385. ]),
 <BarContainer object of 50 artists>)


sns.boxplot(y=Data["Score"])

<AxesSubplot:ylabel='Score'>


plt.hist(Data['Percentage_won'], bins=50)

(array([1., 0., 1., 0., 0., 2., 0., 4., 2., 3., 0., 1., 3., 2., 9., 2., 3.,
        3., 6., 4., 2., 2., 1., 1., 1., 0., 0., 0., 2., 0., 0., 1., 1., 0.,
        0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1.]),
 array([16.66666667, 17.52594738, 18.3852281 , 19.24450881, 20.10378952,
        20.96307024, 21.82235095, 22.68163167, 23.54091238, 24.4001931 ,
        25.25947381, 26.11875453, 26.97803524, 27.83731595, 28.69659667,
        29.55587738, 30.4151581 , 31.27443881, 32.13371953, 32.99300024,
        33.85228096, 34.71156167, 35.57084238, 36.4301231 , 37.28940381,
        38.14868453, 39.00796524, 39.86724596, 40.72652667, 41.58580739,
        42.4450881 , 43.30436881, 44.16364953, 45.02293024, 45.88221096,
        46.74149167, 47.60077239, 48.4600531 , 49.31933382, 50.17861453,
        51.03789524, 51.89717596, 52.75645667, 53.61573739, 54.4750181 ,
        55.33429882, 56.19357953, 57.05286025, 57.91214096, 58.77142168,
        59.63070239]),
 <BarContainer object of 50 artists>)


sns.boxplot(y=Data["Percentage_won"])

<AxesSubplot:ylabel='Percentage_won'>


plt.hist(Data['Percentage_BasketScored'], bins=50)

(array([1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 2., 3., 5., 0., 3., 5.,
        3., 4., 5., 1., 2., 5., 5., 3., 0., 0., 3., 0., 1., 0., 0., 1., 1.,
        0., 0., 1., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 2.]),
 array([27.77777778, 28.53129699, 29.28481621, 30.03833543, 30.79185465,
        31.54537386, 32.29889308, 33.0524123 , 33.80593151, 34.55945073,
        35.31296995, 36.06648917, 36.82000838, 37.5735276 , 38.32704682,
        39.08056603, 39.83408525, 40.58760447, 41.34112368, 42.0946429 ,
        42.84816212, 43.60168134, 44.35520055, 45.10871977, 45.86223899,
        46.6157582 , 47.36927742, 48.12279664, 48.87631585, 49.62983507,
        50.38335429, 51.13687351, 51.89039272, 52.64391194, 53.39743116,
        54.15095037, 54.90446959, 55.65798881, 56.41150802, 57.16502724,
        57.91854646, 58.67206568, 59.42558489, 60.17910411, 60.93262333,
        61.68614254, 62.43966176, 63.19318098, 63.94670019, 64.70021941,
        65.45373863]),
 <BarContainer object of 50 artists>)


sns.boxplot(y=Data["Percentage_BasketScored"])

<AxesSubplot:ylabel='Percentage_BasketScored'>


plt.hist(Data['percentage_Finalist'], bins=50)

(array([47.,  4.,  1.,  0.,  1.,  2.,  0.,  0.,  0.,  0.,  1.,  1.,  0.,
         1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.]),
 array([ 0.        ,  1.30232558,  2.60465116,  3.90697674,  5.20930233,
         6.51162791,  7.81395349,  9.11627907, 10.41860465, 11.72093023,
        13.02325581, 14.3255814 , 15.62790698, 16.93023256, 18.23255814,
        19.53488372, 20.8372093 , 22.13953488, 23.44186047, 24.74418605,
        26.04651163, 27.34883721, 28.65116279, 29.95348837, 31.25581395,
        32.55813953, 33.86046512, 35.1627907 , 36.46511628, 37.76744186,
        39.06976744, 40.37209302, 41.6744186 , 42.97674419, 44.27906977,
        45.58139535, 46.88372093, 48.18604651, 49.48837209, 50.79069767,
        52.09302326, 53.39534884, 54.69767442, 56.        , 57.30232558,
        58.60465116, 59.90697674, 61.20930233, 62.51162791, 63.81395349,
        65.11627907]),
 <BarContainer object of 50 artists>)


sns.boxplot(y=Data["percentage_Finalist"])

<AxesSubplot:ylabel='percentage_Finalist'>


plt.hist(Data['Finalist'], bins=50)

(array([53.,  0.,  0.,  0.,  2.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,
         1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.]),
 array([ 0.  ,  1.12,  2.24,  3.36,  4.48,  5.6 ,  6.72,  7.84,  8.96,
        10.08, 11.2 , 12.32, 13.44, 14.56, 15.68, 16.8 , 17.92, 19.04,
        20.16, 21.28, 22.4 , 23.52, 24.64, 25.76, 26.88, 28.  , 29.12,
        30.24, 31.36, 32.48, 33.6 , 34.72, 35.84, 36.96, 38.08, 39.2 ,
        40.32, 41.44, 42.56, 43.68, 44.8 , 45.92, 47.04, 48.16, 49.28,
        50.4 , 51.52, 52.64, 53.76, 54.88, 56.  ]),
 <BarContainer object of 50 artists>)


plt.hist(Data['TournamentChampion'], bins=50)

(array([52.,  3.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,
         0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,
         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]),
 array([ 0.  ,  0.66,  1.32,  1.98,  2.64,  3.3 ,  3.96,  4.62,  5.28,
         5.94,  6.6 ,  7.26,  7.92,  8.58,  9.24,  9.9 , 10.56, 11.22,
        11.88, 12.54, 13.2 , 13.86, 14.52, 15.18, 15.84, 16.5 , 17.16,
        17.82, 18.48, 19.14, 19.8 , 20.46, 21.12, 21.78, 22.44, 23.1 ,
        23.76, 24.42, 25.08, 25.74, 26.4 , 27.06, 27.72, 28.38, 29.04,
        29.7 , 30.36, 31.02, 31.68, 32.34, 33.  ]),
 <BarContainer object of 50 artists>)


plt.hist(Data['HighestPositionHeld'], bins=50)

(array([9., 0., 5., 0., 0., 4., 0., 6., 0., 0., 4., 0., 0., 5., 0., 5., 0.,
        0., 4., 0., 0., 2., 0., 4., 0., 0., 1., 0., 2., 0., 0., 0., 0., 0.,
        1., 0., 1., 0., 0., 3., 0., 0., 3., 0., 0., 0., 0., 1., 0., 1.]),
 array([ 1.  ,  1.38,  1.76,  2.14,  2.52,  2.9 ,  3.28,  3.66,  4.04,
         4.42,  4.8 ,  5.18,  5.56,  5.94,  6.32,  6.7 ,  7.08,  7.46,
         7.84,  8.22,  8.6 ,  8.98,  9.36,  9.74, 10.12, 10.5 , 10.88,
        11.26, 11.64, 12.02, 12.4 , 12.78, 13.16, 13.54, 13.92, 14.3 ,
        14.68, 15.06, 15.44, 15.82, 16.2 , 16.58, 16.96, 17.34, 17.72,
        18.1 , 18.48, 18.86, 19.24, 19.62, 20.  ]),
 <BarContainer object of 50 artists>)


sns.boxplot(y=Data["HighestPositionHeld"])

<AxesSubplot:ylabel='HighestPositionHeld'>


sns.pairplot(Data, kind='reg')

<seaborn.axisgrid.PairGrid at 0x7f92c2cfdcd0>


fig,ax = plt.subplots(figsize=(50,20)) 
sns.lineplot(Data['Team'], Data['Score'],ax=ax)

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Team', ylabel='Score'>


fig,ax = plt.subplots(figsize=(20,20)) 
sns.heatmap(Data.corr(), annot=True, ax=ax)

<AxesSubplot:>


fig,ax = plt.subplots(figsize=(50,20)) 
sns.lineplot(Data['Team'], Data['Percentage_won'],ax=ax)

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Team', ylabel='Percentage_won'>


Data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 61 entries, 0 to 60
Data columns (total 19 columns):
 #   Column                         Non-Null Count  Dtype  
---  ------                         --------------  -----  
 0   Team                           61 non-null     object 
 1   Tournament                     61 non-null     int64  
 2   Score                          61 non-null     int64  
 3   PlayedGames                    61 non-null     int64  
 4   WonGames                       61 non-null     int64  
 5   DrawnGames                     61 non-null     int64  
 6   LostGames                      61 non-null     int64  
 7   BasketScored                   61 non-null     int64  
 8   BasketGiven                    61 non-null     int64  
 9   TournamentChampion             61 non-null     int64  
 10  Runner-up                      61 non-null     int64  
 11  TeamLaunch                     61 non-null     object 
 12  HighestPositionHeld            61 non-null     int64  
 13  TeamLaunchStartYear            61 non-null     int64  
 14  Percentage_won                 60 non-null     float64
 15  Percentage_BasketScored        60 non-null     float64
 16  Finalist                       61 non-null     int64  
 17  percentage_Finalist            61 non-null     float64
 18  percentage_TournamentChampion  61 non-null     float64
dtypes: float64(4), int64(13), object(2)
memory usage: 9.2+ KB


fig,ax = plt.subplots(figsize=(50,20)) 
sns.lineplot(Data['Team'], Data['percentage_TournamentChampion'],ax=ax)

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Team', ylabel='percentage_TournamentChampion'>


fig,ax = plt.subplots(figsize=(50,20)) 
sns.lineplot(Data['Team'], Data['Percentage_BasketScored'],ax=ax)

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Team', ylabel='Percentage_BasketScored'>


Data[['TeamLaunchStartYear','Score']].groupby(['TeamLaunchStartYear']).sum().plot(figsize=(15,5))

plt.show()


fig,ax = plt.subplots(figsize=(50,20)) 
sns.scatterplot(Data['TeamLaunchStartYear'], Data['Score'],hue= Data['Team'], ax=ax)

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='TeamLaunchStartYear', ylabel='Score'>


fig,ax = plt.subplots(figsize=(50,20)) 
sns.lineplot(Data['Team'], Data['Finalist'],ax=ax)

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Team', ylabel='Finalist'>


fig,ax = plt.subplots(figsize=(50,20)) 
sns.lineplot(Data['Team'], Data['percentage_Finalist'],ax=ax)

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Team', ylabel='percentage_Finalist'>


fig,ax = plt.subplots(figsize=(50,20)) 
sns.lineplot(Data['Team'], Data['TournamentChampion'],ax=ax)

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Team', ylabel='TournamentChampion'>


fig,ax = plt.subplots(figsize=(50,20)) 
sns.lineplot(Data['Team'], Data['HighestPositionHeld'],ax=ax)

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

<AxesSubplot:xlabel='Team', ylabel='HighestPositionHeld'>


#import pandas_profiling
import pandas_profiling 
data_profile  = pd.read_csv('DS - Part2 - Basketball.csv')
pandas_profiling.ProfileReport(data_profile)


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True) # adds a nice background to the graphs
%matplotlib inline


data= pd.read_csv('DS - Part3 - CompanyX_EU.csv') 
data.head()  # view the first 5 rows of the data


data.dtypes

Startup           object
Product           object
Funding           object
Event             object
Result            object
OperatingState    object
dtype: object


data.shape

(662, 6)


data.describe()


data.Result.value_counts()

Contestant         488
Finalist            84
Audience choice     41
Winner              26
Runner up           23
Name: Result, dtype: int64


data.OperatingState.value_counts()

Operating    465
Closed       106
Acquired      86
Ipo            5
Name: OperatingState, dtype: int64


data.Funding.value_counts()

$1M       17
$2M       12
$3M        9
$1.2M      9
$1.3M      9
          ..
$12.8M     1
$64M       1
$35.4M     1
$9.7M      1
$590K      1
Name: Funding, Length: 240, dtype: int64


data.isnull().values.any()

True


data.isnull().sum()

Startup             0
Product             6
Funding           214
Event               0
Result              0
OperatingState      0
dtype: int64


dupes = data.duplicated()
sum(dupes)

0


data.dropna(inplace=True)


data.isnull().sum()

Startup           0
Product           0
Funding           0
Event             0
Result            0
OperatingState    0
dtype: int64


data.isnull().values.any()

False


data.Funding.value_counts()

$1M        17
$2M        12
$1.2M       9
$1.3M       9
$3M         9
           ..
$36.5M      1
$103M       1
$35.4M      1
$5K         1
$684.4K     1
Name: Funding, Length: 239, dtype: int64


data.Funding = data.Funding.apply(lambda x:x.replace('$',''))
data


data.Funding = (data.Funding.replace(r'[KMB]+$', '', regex=True).astype(float) * \
                data.Funding.str.extract(r'[\d\.]+([KMB]+)', expand=False).
                fillna(1).replace(['K','M', 'B'], [10**3, 10**6, 10**9]).astype(int))
data


data["Funding(in Millions)"]=data.Funding/10**6
data


data.describe()


%matplotlib inline
from matplotlib import pyplot as plt

plot = plt.boxplot(data["Funding(in Millions)"])
plt.title('Boxplot of the funds')
plt.ylabel("Funding(in Millions)")

plt.show()


sns.set_theme(style="whitegrid")
sns.set(rc = {'figure.figsize':(15,8)})
sns.boxplot(x=(data["Funding"]/(10**6)),showfliers = False)

<AxesSubplot:xlabel='Funding'>


lower_fence = plot['caps'][0].get_data()[1][1]   
lower_fence

0.005


[item.get_ydata() for item in plot['whiskers']]

[array([0.74525, 0.005  ]), array([ 9.475, 22.   ])]


# Other details

median_Funding = data.Funding.median()
Q1_Funding = data.Funding.quantile(q=0.25)
Q3_Funding = data.Funding.quantile(q=0.75)
min_Funding = data.Funding.min()
IQR = Q3_Funding-Q1_Funding
print(f"Funding : \nMedian : {median_Funding} \nQ1 :{Q1_Funding}\nQ3 : {Q3_Funding} \nMin : {min_Funding} \nIQR : {IQR} ")
print(f"Upper whisker : {Q3_Funding + 1.5* IQR}")
print(f"Lower whisker : {(Q1_Funding - 1.5* IQR) if (Q1_Funding - 1.5* IQR)>0 else 0}")

Funding : 
Median : 2200000.0 
Q1 :745250.0
Q3 : 9475000.0 
Min : 5000.0 
IQR : 8729750.0 
Upper whisker : 22569625.0
Lower whisker : 0


data_Funding_outliers =data.Funding[data.Funding > (Q3_Funding + 1.5 * IQR)]
len(data_Funding_outliers)

60


df_out = data.copy()
# Keeping the values lesser than upper whiskers =Q3_Funding + 1.5 * IQR
df_out = df_out[data.Funding < (Q3_Funding + 1.5 * IQR)] 
df_out.shape
df_out


sns.set_theme(style="whitegrid")
sns.set(rc = {'figure.figsize':(15,8)})
sns.boxplot(x=(df_out["Funding"]/(10**6)))

<AxesSubplot:xlabel='Funding'>


df_out.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 386 entries, 1 to 660
Data columns (total 7 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Startup               386 non-null    object 
 1   Product               386 non-null    object 
 2   Funding               386 non-null    float64
 3   Event                 386 non-null    object 
 4   Result                386 non-null    object 
 5   OperatingState        386 non-null    object 
 6   Funding(in Millions)  386 non-null    float64
dtypes: float64(2), object(5)
memory usage: 24.1+ KB


df_out.OperatingState.value_counts()

Operating    275
Closed        56
Acquired      55
Name: OperatingState, dtype: int64


plt.hist(df_out.OperatingState)

(array([ 56.,   0.,   0.,   0.,   0., 275.,   0.,   0.,   0.,  55.]),
 array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),
 <BarContainer object of 10 artists>)


sns.displot(x=(df_out["Funding(in Millions)"]),bins=30)
plt.title('Distribution plot for Funds in million.')
plt.show()


plt.hist(df_out["Funding"]/(10**6), bins=50)

(array([77., 50., 49., 20., 28., 13., 15., 14.,  6., 10.,  5.,  6.,  5.,
         5.,  6.,  5.,  8.,  2.,  6.,  2.,  1.,  2.,  8.,  2.,  3.,  1.,
         2.,  6.,  3.,  3.,  0.,  0.,  3.,  0.,  1.,  2.,  2.,  3.,  0.,
         1.,  2.,  2.,  1.,  2.,  0.,  0.,  2.,  1.,  0.,  1.]),
 array([5.00000e-03, 4.44900e-01, 8.84800e-01, 1.32470e+00, 1.76460e+00,
        2.20450e+00, 2.64440e+00, 3.08430e+00, 3.52420e+00, 3.96410e+00,
        4.40400e+00, 4.84390e+00, 5.28380e+00, 5.72370e+00, 6.16360e+00,
        6.60350e+00, 7.04340e+00, 7.48330e+00, 7.92320e+00, 8.36310e+00,
        8.80300e+00, 9.24290e+00, 9.68280e+00, 1.01227e+01, 1.05626e+01,
        1.10025e+01, 1.14424e+01, 1.18823e+01, 1.23222e+01, 1.27621e+01,
        1.32020e+01, 1.36419e+01, 1.40818e+01, 1.45217e+01, 1.49616e+01,
        1.54015e+01, 1.58414e+01, 1.62813e+01, 1.67212e+01, 1.71611e+01,
        1.76010e+01, 1.80409e+01, 1.84808e+01, 1.89207e+01, 1.93606e+01,
        1.98005e+01, 2.02404e+01, 2.06803e+01, 2.11202e+01, 2.15601e+01,
        2.20000e+01]),
 <BarContainer object of 50 artists>)


df_out["Funding(in Millions)"].describe()

count    386.00000
mean       3.72514
std        4.73236
min        0.00500
25%        0.60000
50%        1.70000
75%        5.00000
max       22.00000
Name: Funding(in Millions), dtype: float64


# Filter out companies still operating and companies that closed.
df_ops= df_out.OperatingState[(df_out.OperatingState == "Operating") | (df_out.OperatingState == "Closed") ]
df_ops.value_counts()

Operating    275
Closed        56
Name: OperatingState, dtype: int64


fig, ax = plt.subplots(1, 2)

fig.set_figheight(5)
fig.set_figwidth(15)

sns.distplot(df_out.loc[df_out.OperatingState == 'Operating', 'Funding(in Millions)'], ax = ax[0])
sns.distplot(df_out.loc[df_out.OperatingState =='Closed', 'Funding(in Millions)'], ax = ax[1])

ax[0].set_title('Funding in Millions by the companies still operating')
ax[1].set_title('Funding in Millions by companies that got closed')
plt.show()

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


sns.countplot(x =df_ops, palette="Set2")

<AxesSubplot:xlabel='OperatingState', ylabel='count'>


from statsmodels.stats.weightstats import ztest

x1=df_out.Funding[df_out.OperatingState == "Operating"]
x2=df_out.Funding[df_out.OperatingState == "Closed"]
N1 = len(x1)
N2 = len(x2)

alpha = 0.05

test_statistic, p_value = ztest(x1, x2)

if p_value <= alpha:
    print(f'Since the p-value, {round(p_value, 3)} < {alpha} (alpha) the difference is significant and we reject the Null hypothesis')
else:
    print(f"Since the p-value, {round(p_value,3)} > {alpha} (alpha) the difference is not significant and, we fail to reject the Null hypothesis")

Since the p-value, 0.192 > 0.05 (alpha) the difference is not significant and, we fail to reject the Null hypothesis


from scipy.stats import ttest_ind

test_statistic, p_value = ttest_ind(x1, x2)

if p_value <= alpha:
    print(f'Since the p-value, {round(p_value, 3)} < {alpha} (alpha) the difference is significant and we reject the Null hypothesis')
else:
    print(f"Since the p-value, {round(p_value,3)} > {alpha} (alpha) the difference is not significant and, we fail to reject the Null hypothesis")

Since the p-value, 0.193 > 0.05 (alpha) the difference is not significant and, we fail to reject the Null hypothesis


data_new= pd.read_csv('DS - Part3 - CompanyX_EU.csv')
df_c = data_new.copy(deep = True)
df_c.head()


df_c.Result.value_counts()

Contestant         488
Finalist            84
Audience choice     41
Winner              26
Runner up           23
Name: Result, dtype: int64


winners = df_c.Result.value_counts()[1:].sum()
contestants = df_c.Result.value_counts()['Contestant']


contestants_operating = df_c.OperatingState[df_c.Result == 'Contestant'].value_counts().loc['Operating']
winners_operating = df_c.OperatingState[df_c.Result != 'Contestant'].value_counts().loc['Operating']


print(f"percentage of winners that are still operating: {(winners_operating/winners) *100}")
print(f"percentage of contestants that are still operating: {(contestants_operating/contestants)*100}")

percentage of winners that are still operating: 76.4367816091954
percentage of contestants that are still operating: 68.0327868852459


from statsmodels.stats.proportion import proportions_ztest

test_statistic, p_value = proportions_ztest([contestants_operating, winners_operating], [contestants, winners])

if p_value <= alpha:
    print(f'Since the p-value, {round(p_value, 3)} < {alpha} (alpha) the difference is significant and we reject the Null hypothesis')
else:
    print(f"Since the p-value, {round(p_value,3)} > {alpha} (alpha) the difference is not significant and, we fail to reject the Null hypothesis")

Since the p-value, 0.037 < 0.05 (alpha) the difference is significant and we reject the Null hypothesis


df_out.Event.value_counts()

TC50 2008                    25
TC40 2007                    22
Disrupt NY 2015              21
Disrupt NYC 2012             19
Disrupt SF 2014              19
Disrupt SF 2013              19
Disrupt SF 2011              19
Disrupt SF 2015              19
Disrupt NYC 2013             19
TC50 2009                    19
Disrupt SF 2016              17
Disrupt NY 2016              16
Disrupt NYC 2011             15
Disrupt NYC 2014             15
Disrupt SF 2012              15
Disrupt SF 2010              13
Hardware Battlefield 2016    12
Hardware Battlefield 2014    12
Disrupt London 2015          11
Disrupt EU 2014              10
Disrupt NYC 2010             10
Hardware Battlefield 2015    10
Disrupt London 2016          10
Disrupt EU 2013               9
-                             6
Disrupt Beijing 2011          4
Name: Event, dtype: int64


disrupt_events = df_out[df_out.Event.apply(lambda x: 'Disrupt' in x)].Event.value_counts()
disrupt_events

Disrupt NY 2015         21
Disrupt SF 2011         19
Disrupt SF 2014         19
Disrupt SF 2015         19
Disrupt NYC 2012        19
Disrupt SF 2013         19
Disrupt NYC 2013        19
Disrupt SF 2016         17
Disrupt NY 2016         16
Disrupt NYC 2014        15
Disrupt SF 2012         15
Disrupt NYC 2011        15
Disrupt SF 2010         13
Disrupt London 2015     11
Disrupt NYC 2010        10
Disrupt EU 2014         10
Disrupt London 2016     10
Disrupt EU 2013          9
Disrupt Beijing 2011     4
Name: Event, dtype: int64


disrupt_events_2013_grt = df_out[df_out.Event.apply(lambda x: 'Disrupt' in x and int(x[-4:]) > 2012)].Event  
disrupt_events_2013_grt

1         Disrupt NYC 2013
3          Disrupt NY 2016
13         Disrupt SF 2015
14     Disrupt London 2016
16         Disrupt SF 2015
              ...         
635        Disrupt NY 2015
641       Disrupt NYC 2013
642        Disrupt SF 2014
646    Disrupt London 2015
660        Disrupt SF 2013
Name: Event, Length: 185, dtype: object


NY_events = df_out.loc[disrupt_events_2013_grt[disrupt_events_2013_grt.apply(lambda x: 'NY' in x)].index, 'Funding(in Millions)']
SF_events = df_out.loc[disrupt_events_2013_grt[disrupt_events_2013_grt.apply(lambda x: 'SF' in x)].index, 'Funding(in Millions)']
EU_events = df_out.loc[disrupt_events_2013_grt[disrupt_events_2013_grt.apply(lambda x: 'EU' in x or 'London' in x)].index, 'Funding(in Millions)']
print(len(NY_events), len(SF_events), len(EU_events))

71 74 40


from scipy.stats import f_oneway


stat, p_value = f_oneway(NY_events, SF_events, EU_events)

if p_value <= alpha:
    print(f'Since the p-value, {round(p_value, 3)} < {alpha} (alpha) the difference is significant and we reject the Null hypothesis')
else:
    print(f"Since the p-value, {round(p_value,3)} > {alpha} (alpha) the difference is not significant and, we fail to reject the Null hypothesis")

Since the p-value, 0.628 > 0.05 (alpha) the difference is not significant and, we fail to reject the Null hypothesis


plt.figure(figsize=(12,5))
sns.distplot(NY_events, label = 'NY')
sns.distplot(SF_events, label = 'SF')
sns.distplot(EU_events, label = 'EU')
plt.legend()
plt.show()

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


fig, ax = plt.subplots(1, 3)

fig.set_figheight(5)
fig.set_figheight(5)

sns.distplot(NY_events, label = 'NY', ax = ax[0])
sns.distplot(SF_events, label = 'SF', ax = ax[1])
sns.distplot(EU_events, label = 'EU', ax = ax[2])

ax[0].set_title('NY')
ax[1].set_title('SF')
ax[2].set_title('EU')
plt.show()

/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Users/santoshsingh/opt/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


#import pandas_profiling
import pandas_profiling 
pandas_profiling.ProfileReport(data_new)

Planned to purchase Product A	Actually placed and order for Product A - Yes	Actually placed and order for Product A - No	Total
Yes	400	100	500
No	200	1300	1500
Total	600	1400	2000

	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	HighestPositionHeld	TeamLaunchStartYear	Percentage_won	Percentage_BasketScored	Finalist	percentage_Finalist	percentage_TournamentChampion
Tournament	719.700000	2.988113e+04	2.347713e+04	10612.216667	5356.266667	7509.816667	3.941348e+04	3.083912e+04	86.483333	78.666667	-100.233333	-444.716667	172.548918	150.699173	165.150000	199.392048	103.011491
Score	29881.133333	1.287996e+06	9.744279e+05	460619.130874	219506.661749	294342.006557	1.704281e+06	1.247083e+06	4436.231967	3937.989071	-4010.935519	-16950.564481	7821.523453	6814.073405	8374.221038	10051.332600	5260.037711
PlayedGames	23477.133333	9.744279e+05	7.678715e+05	345098.710656	175781.754645	247015.154918	1.280888e+06	1.009673e+06	2756.044809	2518.025137	-3286.984973	-14148.081694	5587.847758	4904.902488	5274.069945	6385.627893	3285.932647
WonGames	10612.216667	4.606191e+05	3.450987e+05	165641.698907	77189.964481	102286.208470	6.128115e+05	4.386830e+05	1675.364208	1473.330328	-1392.963934	-6083.436066	2828.717303	2448.316275	3148.694536	3760.163214	1982.646239
DrawnGames	5356.266667	2.195067e+05	1.757818e+05	77189.964481	40723.028962	57875.583607	2.866269e+05	2.330936e+05	556.011749	518.810656	-766.511202	-3203.622131	1224.962602	1091.152204	1074.822404	1314.123563	666.436653
LostGames	7509.816667	2.943420e+05	2.470152e+05	102286.208470	57875.583607	86853.155191	3.815134e+05	3.379055e+05	524.906557	526.119126	-1127.662842	-4864.037158	1534.570745	1365.855138	1051.025683	1311.948638	637.136562
BasketScored	39413.483333	1.704281e+06	1.280888e+06	612811.511475	286626.939617	381513.436066	2.270266e+06	1.633385e+06	6127.734153	5407.839891	-5163.528689	-22919.254645	10409.944547	9020.750868	11535.574044	13765.597721	7249.982029
BasketGiven	30839.116667	1.247083e+06	1.009673e+06	438683.007650	233093.615301	337905.490710	1.633385e+06	1.354223e+06	3004.783880	2820.337705	-4461.752459	-19692.747541	6902.673882	6051.576632	5825.121585	7119.868702	3601.365568
TournamentChampion	86.483333	4.436232e+03	2.756045e+03	1675.364208	556.011749	524.906557	6.127734e+03	3.004784e+03	29.948634	24.139071	-8.818852	-42.847814	33.058040	27.239488	54.087705	62.899463	34.964972
Runner-up	78.666667	3.937989e+03	2.518025e+03	1473.330328	518.810656	526.119126	5.407840e+03	2.820338e+03	24.139071	20.612568	-8.634153	-39.415847	28.704845	23.941503	44.751639	52.638445	28.258179
HighestPositionHeld	-100.233333	-4.010936e+03	-3.286985e+03	-1392.963934	-766.511202	-1127.662842	-5.163529e+03	-4.461752e+03	-8.818852	-8.634153	27.843169	85.406831	-30.689361	-25.763672	-17.453005	-22.285164	-10.640531
TeamLaunchStartYear	-444.716667	-1.695056e+04	-1.414808e+04	-6083.436066	-3203.622131	-4864.037158	-2.291925e+04	-1.969275e+04	-42.847814	-39.415847	85.406831	755.376503	-102.513872	-71.174010	-82.263661	-98.304990	-51.441691
Percentage_won	172.548918	7.821523e+03	5.587848e+03	2828.717303	1224.962602	1534.570745	1.040994e+04	6.902674e+03	33.058040	28.704845	-30.689361	-102.513872	61.327685	50.399847	61.762885	74.256747	38.991248
Percentage_BasketScored	150.699173	6.814073e+03	4.904902e+03	2448.316275	1091.152204	1365.855138	9.020751e+03	6.051577e+03	27.239488	23.941503	-25.763672	-71.174010	50.399847	45.930644	51.180991	61.709116	32.166062
Finalist	165.150000	8.374221e+03	5.274070e+03	3148.694536	1074.822404	1051.025683	1.153557e+04	5.825122e+03	54.087705	44.751639	-17.453005	-82.263661	61.762885	51.180991	98.839344	115.537908	63.223151
percentage_Finalist	199.392048	1.005133e+04	6.385628e+03	3760.163214	1314.123563	1311.948638	1.376560e+04	7.119869e+03	62.899463	52.638445	-22.285164	-98.304990	74.256747	61.709116	115.537908	136.189029	73.666006
percentage_TournamentChampion	103.011491	5.260038e+03	3.285933e+03	1982.646239	666.436653	637.136562	7.249982e+03	3.601366e+03	34.964972	28.258179	-10.640531	-51.441691	38.991248	32.166062	63.223151	73.666006	40.866261

	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	HighestPositionHeld	TeamLaunchStartYear	Percentage_won	Percentage_BasketScored	Finalist	percentage_Finalist	percentage_TournamentChampion
Tournament	1.000000	0.981441	0.998677	0.971954	0.989387	0.949863	0.975059	0.987828	0.589072	0.645876	-0.708071	-0.603151	0.819559	0.827096	0.619210	0.636885	0.600658
Score	0.981441	1.000000	0.979824	0.997240	0.958452	0.880040	0.996656	0.944263	0.714280	0.764278	-0.669775	-0.543432	0.877385	0.883248	0.742202	0.758919	0.725019
PlayedGames	0.998677	0.979824	1.000000	0.967641	0.994053	0.956503	0.970127	0.990129	0.574716	0.632921	-0.710876	-0.587451	0.813179	0.824801	0.605392	0.624436	0.586586
WonGames	0.971954	0.997240	0.967641	1.000000	0.939844	0.852785	0.999318	0.926234	0.752204	0.797350	-0.648628	-0.543854	0.884278	0.884390	0.778181	0.791682	0.762040
DrawnGames	0.989387	0.958452	0.994053	0.939844	1.000000	0.973156	0.942668	0.992579	0.503472	0.566269	-0.719845	-0.577616	0.774416	0.797102	0.535737	0.558014	0.516602
LostGames	0.949863	0.880040	0.956503	0.852785	0.973156	1.000000	0.859169	0.985275	0.325462	0.393211	-0.725149	-0.600513	0.665366	0.684314	0.358720	0.381463	0.338187
BasketScored	0.975059	0.996656	0.970127	0.999318	0.942668	0.859169	1.000000	0.931548	0.743144	0.790532	-0.649455	-0.553453	0.879124	0.880281	0.770080	0.782863	0.752690
BasketGiven	0.987828	0.944263	0.990129	0.926234	0.992579	0.985275	0.931548	1.000000	0.471824	0.533814	-0.726610	-0.615715	0.757279	0.767157	0.503495	0.524271	0.484105
TournamentChampion	0.589072	0.714280	0.574716	0.752204	0.503472	0.325462	0.743144	0.471824	1.000000	0.971552	-0.305397	-0.284878	0.765351	0.728718	0.994134	0.984889	0.999453
Runner-up	0.645876	0.764278	0.632921	0.797350	0.566269	0.393211	0.790532	0.533814	0.971552	1.000000	-0.360408	-0.315881	0.801247	0.772217	0.991466	0.993496	0.973634
HighestPositionHeld	-0.708071	-0.669775	-0.710876	-0.648628	-0.719845	-0.725149	-0.649455	-0.726610	-0.305397	-0.360408	1.000000	0.588914	-0.737288	-0.715211	-0.332695	-0.361897	-0.315443
TeamLaunchStartYear	-0.603151	-0.543432	-0.587451	-0.543854	-0.577616	-0.600513	-0.553453	-0.615715	-0.284878	-0.315881	0.588914	1.000000	-0.491259	-0.394118	-0.301066	-0.306495	-0.292787
Percentage_won	0.819559	0.877385	0.813179	0.884278	0.774416	0.665366	0.879124	0.757279	0.765351	0.801247	-0.737288	-0.491259	1.000000	0.949620	0.787199	0.806392	0.772811
Percentage_BasketScored	0.827096	0.883248	0.824801	0.884390	0.797102	0.684314	0.880281	0.767157	0.728718	0.772217	-0.715211	-0.394118	0.949620	1.000000	0.753776	0.774349	0.736684
Finalist	0.619210	0.742202	0.605392	0.778181	0.535737	0.358720	0.770080	0.503495	0.994134	0.991466	-0.332695	-0.301066	0.787199	0.753776	1.000000	0.995838	0.994784
percentage_Finalist	0.636885	0.758919	0.624436	0.791682	0.558014	0.381463	0.782863	0.524271	0.984889	0.993496	-0.361897	-0.306495	0.806392	0.774349	0.995838	1.000000	0.987447
percentage_TournamentChampion	0.600658	0.725019	0.586586	0.762040	0.516602	0.338187	0.752690	0.484105	0.999453	0.973634	-0.315443	-0.292787	0.772811	0.736684	0.994784	0.987447	1.000000

	Funding	Funding(in Millions)
count	4.460000e+02	446.000000
mean	1.724149e+07	17.241489
std	9.048371e+07	90.483710
min	5.000000e+03	0.005000
25%	7.452500e+05	0.745250
50%	2.200000e+06	2.200000
75%	9.475000e+06	9.475000
max	1.700000e+09	1700.000000

	Team	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	TeamLaunch	HighestPositionHeld
0	Team 1	86	4385	2762	1647	552	563	5947	3140	33	23	1929	1
1	Team 2	86	4262	2762	1581	573	608	5900	3114	25	25	1929	1
2	Team 3	80	3442	2614	1241	598	775	4534	3309	10	8	1929	1
3	Team 4	82	3386	2664	1187	616	861	4398	3469	6	6	1931to32	1
4	Team 5	86	3368	2762	1209	633	920	4631	3700	8	7	1929	1

	Team	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	TeamLaunch	HighestPositionHeld
56	Team 57	1	34	38	8	10	20	38	66	-	-	2009-10	20
57	Team 58	1	22	30	7	8	15	37	57	-	-	1956-57	16
58	Team 59	1	19	30	7	5	18	51	85	-	-	1951~52	16
59	Team 60	1	14	30	5	4	21	34	65	-	-	1955-56	15
60	Team 61	1	-	-	-	-	-	-	-	-	-	2017~18	9

	Tournament	Score	PlayedGames	WonGames	DrawnGames	LostGames	BasketScored	BasketGiven	TournamentChampion	Runner-up	HighestPositionHeld	TeamLaunchStartYear	Percentage_won	Percentage_BasketScored	Finalist	percentage_Finalist	percentage_TournamentChampion
count	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	61.000000	60.000000	60.000000	61.000000	61.000000	61.000000
mean	24.000000	901.426230	796.819672	303.967213	188.934426	303.754098	1140.344262	1140.229508	1.426230	1.409836	7.081967	1958.918033	31.364790	43.610198	2.836066	3.645135	1.720841
std	26.827225	1134.899121	876.282765	406.991030	201.799477	294.708594	1506.740211	1163.710766	5.472535	4.540107	5.276663	27.484114	7.831199	6.777215	9.941798	11.670006	6.392672
min	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1929.000000	16.666667	27.777778	0.000000	0.000000	0.000000
25%	4.000000	96.000000	114.000000	34.000000	24.000000	62.000000	153.000000	221.000000	0.000000	0.000000	3.000000	1935.000000	27.607494	39.742084	0.000000	0.000000	0.000000
50%	12.000000	375.000000	423.000000	123.000000	95.000000	197.000000	430.000000	632.000000	0.000000	0.000000	6.000000	1951.000000	30.491722	42.399042	0.000000	0.000000	0.000000
75%	38.000000	1351.000000	1318.000000	426.000000	330.000000	563.000000	1642.000000	1951.000000	0.000000	0.000000	10.000000	1978.000000	33.540164	45.493620	0.000000	0.000000	0.000000
max	86.000000	4385.000000	2762.000000	1647.000000	633.000000	1070.000000	5947.000000	3889.000000	33.000000	25.000000	20.000000	2017.000000	59.630702	65.453739	56.000000	65.116279	38.372093

	Startup	Product	Funding	Event	Result	OperatingState
0	2600Hz	2600hz.com	NaN	Disrupt SF 2013	Contestant	Operating
1	3DLT	3dlt.com	$630K	Disrupt NYC 2013	Contestant	Closed
2	3DPrinterOS	3dprinteros.com	NaN	Disrupt SF 2016	Contestant	Operating
3	3Dprintler	3dprintler.com	$1M	Disrupt NY 2016	Audience choice	Operating
4	42 Technologies	42technologies.com	NaN	Disrupt NYC 2013	Contestant	Operating

	Startup	Product	Funding	Event	Result	OperatingState
count	662	656	448	662	662	662
unique	662	656	240	26	5	4
top	DotSpots	artveoli.com	$1M	TC50 2008	Contestant	Operating
freq	1	1	17	52	488	465

	Startup	Product	Funding	Event	Result	OperatingState
1	3DLT	3dlt.com	630K	Disrupt NYC 2013	Contestant	Closed
3	3Dprintler	3dprintler.com	1M	Disrupt NY 2016	Audience choice	Operating
5	5to1	5to1.com	19.3M	TC50 2009	Contestant	Acquired
6	8 Securities	8securities.com	29M	Disrupt Beijing 2011	Finalist	Operating
10	AdhereTech	adheretech.com	1.8M	Hardware Battlefield 2014	Contestant	Operating
...	...	...	...	...	...	...
657	Zivity	zivity.com	8M	TC40 2007	Contestant	Operating
658	Zmorph	zmorph3d.com	1M	-	Audience choice	Operating
659	Zocdoc	zocdoc.com	223M	TC40 2007	Contestant	Operating
660	Zula	zulaapp.com	3.4M	Disrupt SF 2013	Audience choice	Operating
661	Zumper	zumper.com	31.5M	Disrupt SF 2012	Finalist	Operating

	Startup	Product	Funding	Event	Result	OperatingState
1	3DLT	3dlt.com	630000.0	Disrupt NYC 2013	Contestant	Closed
3	3Dprintler	3dprintler.com	1000000.0	Disrupt NY 2016	Audience choice	Operating
5	5to1	5to1.com	19300000.0	TC50 2009	Contestant	Acquired
6	8 Securities	8securities.com	29000000.0	Disrupt Beijing 2011	Finalist	Operating
10	AdhereTech	adheretech.com	1800000.0	Hardware Battlefield 2014	Contestant	Operating
...	...	...	...	...	...	...
657	Zivity	zivity.com	8000000.0	TC40 2007	Contestant	Operating
658	Zmorph	zmorph3d.com	1000000.0	-	Audience choice	Operating
659	Zocdoc	zocdoc.com	223000000.0	TC40 2007	Contestant	Operating
660	Zula	zulaapp.com	3400000.0	Disrupt SF 2013	Audience choice	Operating
661	Zumper	zumper.com	31500000.0	Disrupt SF 2012	Finalist	Operating

-------------------------------------------------------------------------------------------¶

PART 1¶

-------------------------------------------------------------------------------------------¶

Question:1¶

Question:2¶

Question:3¶

Question: 4¶

ANSWER¶

Question 5¶

Question 6¶

-------------------------------------------------------------------------------------------¶

PART TWO :¶

-------------------------------------------------------------------------------------------¶

PART TWO :¶

EDA for PART TWO :¶

2.1 Load libraries :¶

2.2. Import the dataset(DS - Part2 - Basketball.csv):¶

2.3 Check the Dimension of data?¶

2.4 Check the Information about the data and the datatypes of each respective attributes.¶

2.5 Data preprocessing¶

Analysed basic structure of data¶

Now we have completed the data filtering and processing.¶

We have removed duplicate and null data.¶

Replaced data.¶

Processed the duration to start year.¶

Changed data type.¶

Introduced new columns for better analysis.¶

We are not sure here about the missing data '-' , which could have been improved with some numberical value. Columns like TournamentChampion and Runner-up have lots of missing data which makes it difficult to analyse.¶

2.6 Descriptive Statistics¶

Mean¶

Median¶

Quartiles¶

IQR¶

Range¶

Variance¶

Standard Deviation¶

Check Covariance and Correlation¶

It can be seen that score is highly correlated to most of the variables shown in correlation graph¶

As shown in heat map all the variable except Highest Position held and Team launch start year all the data are related directly and Highest Position held and Team launch start year is inversely related as expected¶

Check the Skewness¶

Univariate Analysis¶

Multivariate analysis¶

Summary :¶

ANALYSIS¶

Descriptive Statistics¶

Univariate Analysis¶

Multivariate analysis¶

Data preprocessing¶

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PART 3¶

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1.1 Data warehouse: Read the CSV file.¶

2.1 Check the datatypes of each attribute.¶

2.2 Check for null values in the attributes.¶

3 Data preprocessing & visualisation¶

3.1 Drop the null values.¶

3.2 Convert the ‘Funding’ features to a numerical value.¶

3.3 Plot box plot for funds in million.¶

3.4 Get the lower fence from the box plot.¶

3.5 Check number of outliers greater than upper fence.¶

3.6 Drop the values that are greater than upper fence.¶

3.7 Plot the box plot after dropping the values.¶

3.8 Check frequency of the OperatingState features classes.¶

3.9 Plot a distribution plot for Funds in million.¶

3.10 Plot distribution plots for companies still operating and companies that closed.¶

4 Statistical analysis:¶

4.1 Is there any significant difference between Funds raised by companies that are still operating vs companies that closed down?¶

4.2 Make a copy of the original data frame.¶

4.3 Check frequency distribution of Result variable.¶

4.4 Calculate percentage of winners that are still operating and percentage of contestants that are still operating¶

4.5 • Write your hypothesis comparing the proportion of companies that are operating between winners and contestants:¶

4.6 Check distribution of the Event variable.¶

4.7 Select only the Event that has disrupt keyword from 2013 onwards.¶

4.8 Write and perform your hypothesis along with significance test comparing the funds raised by companies across NY, SF and EU events from 2013 onwards.¶

4.9 • Plot the distribution plot comparing the 3 city events.¶

5 Write your observations on improvements or suggestions on quality, quantity, variety, velocity, veracity etc. on the data points collected to perform a better data analysis.¶